InSaAF: Incorporating Safety through Accuracy and Fairness | Are LLMs ready for the Indian Legal Domain?

no code implementations • 16 Feb 2024 • Yogesh Tripathi, Raghav Donakanti, Sahil Girhepuje, Ishan Kavathekar, Bhaskara Hanuma Vedula, Gokul S Krishnan, Shreya Goyal, Anmol Goel, Balaraman Ravindran, Ponnurangam Kumaraguru

Task performance and fairness scores of LLaMA and LLaMA--2 models indicate that the proposed $LSS_{\beta}$ metric can effectively determine the readiness of a model for safe usage in the legal sector.

Fairness

RE-GAINS & EnCHANT: Intelligent Tool Manipulation Systems For Enhanced Query Responses

no code implementations • 28 Jan 2024 • Sahil Girhepuje, Siva Sankar Sajeev, Purvam Jain, Arya Sikder, Adithya Rama Varma, Ryan George, Akshay Govind Srinivasan, Mahendra Kurup, Ashmit Sinha, Sudip Mondal

Despite the remarkable success of LLMs, they still suffer from tool invocation and tool chaining due to inadequate input queries and/or tool argument descriptions.

Identifying and examining machine learning biases on Adult dataset

no code implementations • 13 Oct 2023 • Sahil Girhepuje

This research delves into the reduction of machine learning model bias through Ensemble Learning.

Attribute Ensemble Learning +1

Are Models Trained on Indian Legal Data Fair?

no code implementations • 13 Mar 2023 • Sahil Girhepuje, Anmol Goel, Gokul S Krishnan, Shreya Goyal, Satyendra Pandey, Ponnurangam Kumaraguru, Balaraman Ravindran

We highlight the propagation of learnt algorithmic biases in the bail prediction task for models trained on Hindi legal documents.

Fairness